






"""simple docstring"""


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				int      =  ["""pixel_values"""]
					def __init__(							self							:	List[str]     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Dict[str, int] = None     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BICUBIC     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Dict[str, int] = None     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Union[int, float] = 1 / 255     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     , UpperCAmelCase							:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     , **UpperCAmelCase							:	str     , )	->       None:
											super().__init__(**UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			size if size is not None else {'shortest_edge': 224}
											lowerCAmelCase   :Any						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
											lowerCAmelCase   :int						=			get_size_dict(UpperCAmelCase     , param_name='crop_size'   )

											lowerCAmelCase   :Optional[int]						=			do_resize
											lowerCAmelCase   :Dict						=			size
											lowerCAmelCase   :Any						=			resample
											lowerCAmelCase   :Optional[Any]						=			do_center_crop
											lowerCAmelCase   :str						=			crop_size
											lowerCAmelCase   :Tuple						=			do_rescale
											lowerCAmelCase   :Optional[int]						=			rescale_factor
											lowerCAmelCase   :Optional[Any]						=			do_normalize
											lowerCAmelCase   :int						=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											lowerCAmelCase   :int						=			image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Dict[str, int]     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BICUBIC     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Union[str, Any]     , )	->       np.ndarray:
											lowerCAmelCase   :Optional[Any]						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )
											# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
											if "shortest_edge" in size:
																	lowerCAmelCase   :Optional[Any]						=			int((256 / 224) * size['shortest_edge']   )
																	lowerCAmelCase   :Tuple						=			get_resize_output_image_size(UpperCAmelCase     , size=UpperCAmelCase     , default_to_square=UpperCAmelCase   )
																	lowerCAmelCase   :Optional[Any]						=			{'height': output_size[0], 'width': output_size[1]}
											if "height" not in size_dict or "width" not in size_dict:
																	raise ValueError(
																	    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""   )
											return resize(
											    UpperCAmelCase     , size=(size_dict['height'], size_dict['width'])     , resample=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Dict[str, int]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Dict     , )	->       np.ndarray:
											lowerCAmelCase   :List[str]						=			get_size_dict(UpperCAmelCase   )
											if "height" not in size or "width" not in size:
																	raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""   )
											return center_crop(UpperCAmelCase     , size=(size['height'], size['width'])     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[int, float]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	List[str]     , )	->       np.ndarray:
											return rescale(UpperCAmelCase     , scale=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Dict     , )	->       np.ndarray:
											return normalize(UpperCAmelCase     , mean=UpperCAmelCase     , std=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	ImageInput     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[Dict[str, int]] = None     , UpperCAmelCase							:	PILImageResampling = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[Dict[str, int]] = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[float] = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[Union[float, Iterable[float]]] = None     , UpperCAmelCase							:	Optional[Union[float, Iterable[float]]] = None     , UpperCAmelCase							:	Optional[TensorType] = None     , UpperCAmelCase							:	ChannelDimension = ChannelDimension.FIRST     , **UpperCAmelCase							:	Tuple     , )	->       BatchFeature:
											lowerCAmelCase   :int						=			do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   :List[Any]						=			resample if resample is not None else self.resample
											lowerCAmelCase   :Any						=			do_center_crop if do_center_crop is not None else self.do_center_crop
											lowerCAmelCase   :Tuple						=			do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   :Tuple						=			rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCAmelCase   :Tuple						=			do_normalize if do_normalize is not None else self.do_normalize
											lowerCAmelCase   :str						=			image_mean if image_mean is not None else self.image_mean
											lowerCAmelCase   :Any						=			image_std if image_std is not None else self.image_std

											lowerCAmelCase   :Dict						=			size if size is not None else self.size
											lowerCAmelCase   :Any						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )
											lowerCAmelCase   :int						=			crop_size if crop_size is not None else self.crop_size
											lowerCAmelCase   :List[Any]						=			get_size_dict(UpperCAmelCase     , param_name='crop_size'   )

											lowerCAmelCase   :Any						=			make_list_of_images(UpperCAmelCase   )

											if not valid_images(UpperCAmelCase   ):
																	raise ValueError(
																	    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																	    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

											if do_resize and size is None:
																	raise ValueError('Size must be specified if do_resize is True.'   )

											if do_center_crop and crop_size is None:
																	raise ValueError('Crop size must be specified if do_center_crop is True.'   )

											if do_rescale and rescale_factor is None:
																	raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

											if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

											# All transformations expect numpy arrays.
											lowerCAmelCase   :Optional[Any]						=			[to_numpy_array(UpperCAmelCase   ) for image in images]

											if do_resize:
																	lowerCAmelCase   :Dict						=			[self.resize(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											if do_center_crop:
																	lowerCAmelCase   :int						=			[self.center_crop(UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											if do_rescale:
																	lowerCAmelCase   :int						=			[self.rescale(UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											if do_normalize:
																	lowerCAmelCase   :int						=			[self.normalize(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :Tuple						=			[to_channel_dimension_format(UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :Any						=			{'pixel_values': images}
											return BatchFeature(data=UpperCAmelCase     , tensor_type=UpperCAmelCase   )







"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")